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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By : Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan
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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By: Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan

Overview of this book

The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.
Table of Contents (18 chapters)
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Chapter 1: Getting Started with Generative AI
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Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
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Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
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Chapter 7: Useful Frameworks, Libraries, and APIs
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Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
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Chapter 9: LLM Output Evaluation
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Chapter 10: Refining the Semantic Data Model to Improve Accuracy
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Chapter 12: Correcting and Optimizing Your Generative AI Application
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Other Books You May Enjoy
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Appendix: Further Reading: Index
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ANNs for natural language processing

The previous section showed how ANNs can learn mappings of numerical inputs to numerical outputs. Language, however, is inherently non-numeric: a sentence is a sequence of discrete words from a large vocabulary. Building a neural network-based word predictor poses the following challenges:

  • The inputs to the model are discrete words. Since ANNs operate on numeric inputs and outputs, a suitable mapping from words to numbers and vice versa is required.
  • The inputs are further sequential. Unlike bigrams, the model should be able to take more than one word into account when predicting the next word.
  • The output of the language model needs to be a probability distribution over all possible next words. To form a proper distribution, the outputs need to be normalized to be non-negative and sum up to one.

The following sections will explain these challenges and review how they are addressed in modern language models.

Tokenization

...

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